2) takes as input a single chain of documents {ĥ 1 , • • • ,ĥ T } from one of the chains in the 'reasoning tree' created by the DE, and tries to predict a candidate answer from the last documentĥ T in that reasoning chain.

we adopt an LSTM-RNN with an attention mechanism (Bahdanau et al., 2015) to encode theĥ T to ancestor-aware representations y by attending to [ĥ 1,...,T −1 ].


Since the Document Explorer samples documents from a distribution, we use weak supervision at the first and the final hops to account for the otherwise non-differentiabilty in the case of end-to-end training.

we use the document having the shortest TF-IDF distance w.r.t.


We explore an alternative to Evidence Assembler (EA), where instead of selecting key sentences from every root-to-leaf path in the reasoning tree, we use a reranker to rescore the selected documents.

given a document reasoning-tree of t w reasoning chains, we use bidirectional attention (Seo et al., 2017) between the last documents in each chain and all the documents from the previous hops in that chain to obtain {ĥ 1 , • • • ,ĥ tw } which are the refined representations of the leaf documents.


Then we use cv and c i to bias the BiAttn described in the single-hop baseline.


Adversary Failure Analysis Finally, we investigate those "model-successes", where the adversarial examples fail to fool the model.

we find that some questions can be answered with a single document.


Notably, our path extraction method can be easily extended for more hops.

for mhop reasoning, steps (b) and (c) are repeated (m − 1) times, where the intermediate entity from step (c) becomes the head entity for the subsequent step (b).


In contrast, only 46% of three randomly selected paths per question made sense, even when limited to the paths for the correct answers.

a random baseline, even with oracle knowledge of the correct answer, would only find a good path in 46% Table 5: Two top-scoring paths for sample WikiHop Dev questions.


(2015) and use the Euclidean The WMD is then the solution to the linear program: ×V is a nonnegative matrix, where each T i,j denotes how much of word i (across all its tokens) in A is assigned to tokens of word j in B, and the constraints ensure the flow of a given word cannot exceed its weight.

wMD ensures that the entire outgoing flow from word i Additionally, the amount of incoming flow to word j must match .


As a novel component, half of the domain expert annotators receive automatically generated annotation suggestions.

the annotation interface features texts with (suggested) annotations rather than raw texts.


We find that this agreement is similar to the agreement within the STANDARD group for both MeD and TeD.

an annotator with and an annotator without suggestions have the same level of agreement as two annotators without suggestions.


Discussion Our annotation study shows that annotation suggestions have various positive effects on the annotation of epistemic activities, despite the mediocre performance of our suggestion models.

the agreement between annotators in TeD is increased without inducing a noteworthy annotation bias, and annotation time decreases in both MeD and TeD.


Our goal is to close this gap and propose a simple and effective comparison tool between two DNNs based on their test set score distributions.

we make four contributions: Defining a DNN comparison framework: We define three criteria that a DNN comparison tool should meet: (a) Since we observe only a sample from the population score distribution of each model, the decision should be significant under well justified statistical assumptions.


A new comparison tool ( § 4): We propose a solution that meets our three criteria.

we adapt to our problem the recently presented concept of Almost Stochastic Order (ASO) between two distributions (Álvarez-Esteban et al., 2017), 2 and the statistical significance test for this property, which makes very modest assumptions about the participating distributions (criterion (a)).


Stochastic Order (SO) Lehmann (1955) defines a random variable X to be stochastically larger than a random variable Y (denoted by X Y ) if F (a) ≤ G(a) for all a (with a strict inequality for some values of a), where F and G are the CDFs of X and Y , respectively.

if we observe a random value sampled from the first distribution, it is likely to be larger than a random value sampled from the second distribution.


We next discuss different definitions of such relaxation.

a Potential Relaxation For > 0 and random variables X and Y with CDFs F and G, respectively, we can define the following notion ofstochastic dominance: we allow the distributions to violate the stochastic order, and hence one CDF does not have to be strictly below the other for all a.


However, according to this relaxation, X ∼ F is indeed stochastically larger than Y ∼ G. Almost Stochastic Dominance To overcome the limitations of the above straight forward approach, and define a relaxation of stochastic order, we turn to a definition that is based on the proportion of points in the domain of the participating distributions for which SO holds.

the test we will introduce below is based on the following two violation sets: Intuitively, the variable with the smaller violation set should be declared superior and the ratio between these sets should define the gap between the distributions.


In practice we compute the integral operation using the definition of the Riemann integral.

when computing 1 0 f (t)dt we partition the interval between 0 and 1 into small parts of size ∆ and compute the sum of the function value in this part times ∆).


We hypothesize that both the context of the ongoing conversations and the users' previous chatting history will affect their continued interests in future engagement.

we propose a neural framework with three main layers, each modeling context, user history, and interactions between them, to explore how the conversation context and user chatting history jointly result in their re-entry behavior.


All models produce AUC and F1 scores below 70.

models built on rules and features with shallow content and network features perform poorly, suggesting the need of better understanding of conversations or more information like user's chatting history.


BIL-STM+BIA achieves the best AUC and F1 scores, significantly outperforming all other comparison models on both datasets.

it beats BILSTM+MEM, which also able to learn the interaction between user history and conversation content, indicating the effectiveness of bi-attention over memory networks in this task.


Our dataset is produced as a multilingual resource to allow for cross lingual studies and approaches.

it contains hate speech/counter-narrative pairs for English, French, and Italian.


Their work amounted to roughly 200 hours.

they were asked to (i) paraphrase original hate content to augment the number of pairs per language, (ii) annotate hate speech subtopics and counter-narrative types (iii) translate content from French and Italian to English to have parallel data across languages.


ImageNet (Deng et al., 2009) is a visual database developed for object recognition research and consists of 1000 object types.

we use the popular pre-trained InceptionNet model (Szegedy et al., 2015), which achieved the best performance at the ImageNet Large Scale Visual Recognition Challenge 2014 to build the following two imagebased models.


An example of a text passage and its corresponding annotation is presented in the following sentence: ( Annotators mark the claim and the actor, classify the claim as (a subtype of) C3, integration, link them, and mark the position (support/opposition).

tasks 1-5 from Section 4 are all carried 1 For the full codebook, see the supplementary material.


Due to space restrictions, this paper only reports on first steps towards computational construction of discourse networks.

we present pilot models for Tasks 1 and 4 (claim identification and attribution), the two tasks for which we also presented reliability analyses in Section 5.


, e t r |h t | into ten contiguous partitions and multiply each vector with the index of the partition it belongs to.

we multiply e t r i by i % |h t | + 1, where % is the modulus operator.


217 out of the 509 tweets that were annotated manually as non-sarcastic contained such a tag.


We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases.

our approach is based on the divergence between the conditional probability distributions over entity pairs.


In this paper, we evaluate the similarity between relations based on their conditional distributions over entity pairs.

we adopt Kullback-Leibler (KL) divergence of both directions as the metric.


In this subsection, we propose a toy environment to verify our similarity-based method.

we construct a dataset from Wikidata12 and implement Chinese restaurant process13 to split every relation in the dataset into several sub-relations.


A Proofs to theorems in the paper If we have a proposal distribution q(x) satisfying ∀x, f (x) = 1 ∧f (x) = 1 ⇒ q(x) = 0, then equation (16) can be further written as Sometimes, it's hard for us to compute normalized probability q.

to tackle this problem, consider selfnormalized importance sampling as an unbiased estimation (Owen, 2013), whereŵ i is the normalized version of w. for a relation r with currently m subrelations, we turn it to a new sub-relation with probability p = α α + n + 1 (19) or to the k th existing sub-relation with probability where n k is the size of k th existing sub-relation, n is the sum of the number of all sub-relationships of r, and α is a hyperparameter, in which case we use α = 1.


For this type of task, we evaluate on the FewRel (Han et al., 2018) dataset.

we are given K sets of N labeled relation statements where t i ∈ {1 .


To avoid simply relearning the entity linking system, we introduce a modified corpus where contains a relation statement in which one or both entity mentions may have been replaced by a special [BLANK] symbol.

,x contains the span defined by s 1 with probability α.


Common sources of model instability in modern NLP include weight initialisation, data sub-sampling for stochastic gradient calculation, negative sampling used to train word embeddings (Mikolov et al., 2013) and feature sub-sampling for ensemble methods.

the often state-of-the-art LSTMs (and its many variants) have been shown to exhibit high sensitivity to random seeds (Reimers and Gurevych, 2017).


Each arm produces rewards from different random distributions which the user must learn by pulling arms.

model selection is equivalent to the problem of best-arm-identification; identifying the arm with the highest mean.


As expected, performance degrades as we increase batch size, with batches of four allowing more fine grained control over model evaluations than using batches of eight.

due to the exploitative nature of Thompson sampling, we see that selecting models to a very high confidence (95%) requires more computation with BTS than the standard non-adaptive approach.


tion of attention, 4 which is used by most models, including the HAN.

we use the additive formulation originally defined in Bahdanau et al.


The less concise these explanations get, and the farther down the ranking that the items truly driving the model's decision fall, the less likely it becomes for that ranking to truly describe importance.

we expect that the top items in a truly useful ranking of importance would comprise a minimal necessary set of information for making the model's decision.


RSA finds connections between data from two different representation spaces.

for each representation type we compute a matrix of similarities between pairs of stimuli.


We can now fit a multivariate linear regression model to map between the two views: where k is the source and l is the target view, and MSE is the mean squared error.

the success of this model can be seen as an indication of how predictable representation l is from representation k. we use a cross-validated Pearson's correlation between predicted and true targets for an L 2 -penalized model.


In text classification, an input x is mapped to a distribution over target labels: where we have a neural network architecture f (•; θ) parameterize the model-θ collectively denotes the parameters of the NN layers in f .


The resulting variable is defined over the closed interval [0, 1].

note that while there is 0 probability of sampling t = 0, sampling h = 0 corresponds to sampling any t ∈ (l, 0], a set whose mass under Kuma(t|a, b, l, r) is available in closed form: because all negative values of t are deterministically mapped to zero.


We introduce the notion of recurrence into our deep self-attention network.

instead of computing the hidden states from scratch for each new segment, we reuse the hidden states obtained in previous segments.


In this work, we stick to the standard neural approach to modeling the conditional probability.

a trainable neural network is used to encode the context x <t into a fixed size hidden state, which is multiplied with the word embeddings to obtain the logits.


Besides achieving extra long context and resolving fragmentation, another benefit that comes with the recurrence scheme is significantly faster evaluation.

during evaluation, the representations from the previous segments can be reused instead of being computed from scratch as in the case of the vanilla model.


While we found the idea presented in the previous subsection very appealing, there is a crucial technical challenge we haven't solved in or-der to reuse the hidden states.

how can we keep the positional information coherent when we reuse the states?


Although Transformer-XL is mainly designed to better capture longer-term dependency, it dramatically improves the single-model SoTA from 23.7 to 21.8.

transformer-XL significantly outperforms a contemporary method using vanilla transformers (Baevski and Auli, 2018), suggesting the advantage of transformer-XL is generalizable to modeling short sequences.


In order to achieve this goal, we deliberately choose a dataset that does not require longterm dependency, so that any improvement from establishing the recurrence can be attributed to solving the context fragmentation.

we perform this controlled experiment on the One Billion Word dataset, which can only benefit from removing the context fragmentation.


To remedy this problem, we propose an unsupervised adaptation method which finetunes a pre-trained out-of-domain NMT model using a pseudo-in-domain corpus.

we perform lexicon induction to extract an in-domain lexicon, and construct a pseudo-parallel in-domain corpus by performing word-for-word back-translation of monolingual in-domain target sentences.


On the other hand, data-based methods perform adaptation either by combining in-domain and out-of-domain parallel corpora for supervised adaptation (Luong and Manning, 2015;Freitag and Al-Onaizan, 2016) or by generating pseudo-parallel corpora from indomain monolingual data for unsupervised adaptation (Sennrich et al., 2016a;Currey et al., 2017).

in this paper we tackle the task of data-based, unsupervised adaptation, where representative methods include creation of a pseudoparallel corpus by back-translation of in-domain monolingual target sentences (Sennrich et al., 2016a), or construction of a pseudo-parallel indomain corpus by copying monolingual target sentences to the source side (Currey et al., 2017).


Finally, we use this lexicon to create pseudoparallel in-domain data to train NMT models.


First, we compare the percentage of successful in-domain word translations across all adaptation methods.

we scan the source and reference of the test set to count the number of valid hits C, then scan the output file to get the count C t in the same way.


In order to examine this hypothesis, we do experiments using pseudo-in-domain training sets generated by our induced lexicon with various coverage levels.

we split the lexicon into 5 folds randomly and use a portion of it comprising folds 1 through 5, which correspond to 20%, 40%, 60%, 80% and 100% of the original data.


As the number of real in-domain sentences increases, the BLEU scores on the in-domain test set improve, and finetuning on both the pseudo and real in-domain sentences further improves over fine-tuning sorely on the real in-domain sentences.

given a reasonable number of real in-domain sentences in a common semi-supervised adaptation setting, DALI is still helpful in leveraging a large number of monolingual in-domain sentences.


Instead of storing the detailed sentences or translation history, we propose to generate representations containing global monolingual and bilingual contextual information with Local Coordinate Coding (LCC) (Yu et al., 2009).

for solution (1), the hidden states of NMT encoder are coded by a linear combination of a set of anchor points in an unsupervised manner.


Therefore, during translation decoding of NMT, we can drop the coefficient γ and rephrase the source sentence only with the anchor points.

we apply an attention mechanism between current local contextual information and each anchor point v j to get the global context as: where α G tj is the attention score between current local contexts and the global context, computed as: Once the global context c G t is obtained, we feed it to decoder states: where c t encodes the local contextual information and c G t contains the global monolingual information from all source sentences in the training corpus.


The r(y t ) can be estimated by Monte Carlo sampling, as illustrated in algorithm 1.

we fix y t in position t and sample other words from the probability distribution p(•|X, θ)) for n times.


The case shows that the translation quality rise in the order of NAT-Base, +Reinforce-NAT, FS-decoder to AR-Base and the performance gap is large between NAT-Base and other models.

nAT models suffer from over-translation and und noch tragischer ist , dass es Oxford war -eine Universitt , die nicht nur 14 Tory-Premierminister hervorbrachte , sondern sich bis heute hinter einem unverdienten Ruf von Gleichberechtigung und Gedankenfreiheit versteckt .


(2017), might be seen as special cases in this framework, we note that only their decoders are prefix-to-prefix, while their training is still fullsentence-based.

they use a fullsentence translation model to do simultaneous decoding, which is a mismatch between training and testing.


Using our notation, for a policy g(•), the per-step CW at step t is CW g (t) = g(t)−g(t−1).

the CW of a sentence-pair (x, y) is the average CW over all consecutive wait segments: CW measures the average source segment length (the best case is 1 for wordby-word translation or our wait-1 and the worst case is |x| for full-sentence Mt).


Figure 2 shows that Transformer with hard attention is more effective in handling the long sequences.

the performance gap between our model (THA) and the original Transformer model (TSA) grows bigger as sequences become longer.


In our experiments, we use Transformer as baseline.

we use the PyTorch version (Py-Torch 0.4.0) of OpenNMT.


Pre-training these large language models has largely improved the state of the art of the GLUE benchmark (Wang et al., 2018).

our work uses the BERT pre-trained model and makes use of the representations obtained not only in the encoder but also on the decoder in a language generation task.


To obtain this result, the transfer learning capabilities of BERT were not enough and further engineering effort was required.

a conservativeness factor was added during beam decoding to constrain the changes the APE system can make to the mt output.


By modifying the training procedure of neural MT we are completely eliminating any computational overhead at inference time.

the NMT model is trained to learn how to use terminology entries when they are provided as additional input to the source sentence.


We propose an integrated approach in which the MT model learns, at training time, how to use terminology when target terms are provided in input.

the model should learn to bias the translation to contain the provided terms, even if they were not observed in the training data.


To address this argument, we propose a hybrid attention mechanism to dynamically leverage both of the local and global information.

our approach uses a gating scalar for integrating both sources of the information, which is also convenient for quantifying their contributions.


By integrating with the global information, the hybrid models progressively improves the translation quality, confirming that the local and global information are complementary to each other.

we investigate two combination methods: one uses gating scalar, the other simply concatenates the two sources of information.


Inspired by these studies, we establish a sentence-level agreement channel directly in the deep neural network to shorten the distance between the source and target sentence-level embeddings.

our model can be effectively applied to NMT in two aspects: • Sentence-Level Agreement as Training Objective: we use the sentence-level agreement as a part of the training objective function.


First, we map H enc to the target-side vector EH enc through a simple feed forward network TFFN by eq.

2: we use a Tanh activation function instead of ReLU in the feed forward network.


However, it has greatly improved the translation performance.

by comparing Row 3 and 4, we find that our proposed methods achieve a similar performance with the Transformer(Big) and gain a faster speed with fewer parameters.


Our proposed approach needs only 2 * (n − 1) back-translations in each iteration during training.

we train an NMT system, using only monolingual corpora, for 6 translation directions using 4 languages (English, French, German and Spanish) to perform translation in 12 directions.


During training, the expert gate is trained to predict the language of a sample using the gate loss J g , where the expert gate output α is treated as the softmax probability of the predicted languages.

the more accurate the language prediction is, the more the correct expert gets used.


MT baselines employ machine translation (MT) for cross-lingual transfer.

the trainon-trans(lation) method translates the entire English training set into each target language which are in turn used to train a supervised system on the target language.


Bidirectional LSTM is used in the feature extractors for all experiments.

f s is a two-layer BiLSTM of hidden size 128 (64 for each direction), and f p is a two-layer BiLSTM of hidden size 128 stacked with a MoE module (see figure 2).


The conditions are: Languages to align are linguistically similar; Monolingual word embeddings are trained by the same algorithms; And the domains of the monolingual corpora are similar across languages.

the last condition is hard to assume when dealing with resource-poor languages, for which unsupervised methods can be beneficial in reality.


In the cross-lingual experiments, we used 1000 unique pairs of words in the dictionaries and we report p@5 in each language.

for each word in the 1000 source words, we extracted the 5 most similar words from the 1000 target words and checked how often the correct translation is included in them.


In the multilingual experiments, we extracted 500 words aligned among English, French, Spanish and German and evaluated p@5 of 'joint' alignment among the four languages.

for each English word we extracted the 5 most similar words in French, German and Spanish independently, and evaluated how often the correct translation of the English word is included in all of the three languages.


The figure clearly shows that the word embeddings obtained by (Chen and Cardie, 2018) form some clusters based on their languages.

many of the German words are mapped near the centre of the figure and make a large cluster.


Importantly, these features require the dataset to already be available for processing and thus are less conducive to use in situations where resources have not yet been acquired.

we examine the following categories: Dataset Size: We denote the number of training examples in the transfer and task languages by s tf and s tk , respectively.


Dataset-independent features are measures of the similarity between a pair of languages based on phylogenetic or typological properties established by linguistic study.

we leverage six different linguistic distances queried from the URIEL Typological Database (Littell et al., 2017): The orthodromic distance between the languages on the surface of the earth, divided by the antipodal distance, based primarily on language location descriptions in Glottolog (Hammarström et al., 2018).


The use of WikTra with respect to existing tools is justified by a need for high-quality results that also cover complex cases of orthography, e.g., in Semitic scripts where vowels are typically omitted.

junidecode 5 is a character-based transliterator, an approach that seriously limits its accuracy.


As a further abstraction from lexicon size, we introduce the notion of cognate density, defined over a set of words as the ratio of words covered by at least one cognate pair of CogNet.

working with cognate densities allows us to characterise the 'cognate content' of each language independently of the wordnet size.


We encapsulate these basic decipherment principles into a single generative framework.

we introduce a latent variable F = {f i,j } that represents the word-level alignment between the words in the lost language X = {x i } and those in the known language Y = {y j }.


To bypass this issue, we adopt an EMstyle iterative training regime.

the training process involves two interleaving steps.


We use a character-based sequence-to-sequence (seq2seq) model to incorporate the local constraints ( Figure 1).

we integrate Property 1 by using a shared universal character embedding space and a residual connection.


Universal character embedding We directly require that character embeddings of the two languages reside in the same space.

we assume that any character embedding in a given language is a linear combination of universal embeddings.


Residual connection Character alignment is mostly local in nature, but this fact is not reflected by how the next character is predicted by the model.

the prediction is made based on the context vectorh, which is a nonlinear function of the hidden states of the encoder and the decoder.


To address this issue and directly improve the quality of character alignment, we add a residual connection from the encoder embedding layer to the decoder projection layer.

letting α be the predicted attention weights, we compute where E x (i) is the encoder character embedding at position i, and c is the weighted character embedding.ĥ is subsequently used to predict the next character.


Monotonic alignment regularization We design a regularization term that guides the model towards monotonic rewriting.

we penalizes the model whenever insertions or deletions occur.


For Linear B, we modify this regularization term to accommodate the fact that it is a syllabic language and usually one linear B script corresponds to two Greek letters.

we use the following regularization term for Linear B Figure 2 illustrates one alignment matrix from Linear B to Greek.


The latent variable F captures the global constraints as stated in Property 3 and 4.

f should identify a reasonable number of cognate pairs between the two languages, while meeting the requirement that word-level alignments are one-to-one.


To provide a reasonable coverage of the cognate pairs, we further specify the demand constraint j f j,t = D with a given hyperparameter D. We note that the edit distance cost plays an essential role of complementing the neural model.

neural seq2seq models are notoriously inadequate at capturing insertions and deletions, contributing to many issues of overgeneration or undergeneration in NMT (Tu et al., 2016).


Using edit distance in the flow setup helps alleviate this issue, since a misstep of insertion or deletion by the neural model will still generate a string that resembles the ground truth in terms of edit distance.

the edit distance based flow can still recover from the mistakes the neural model makes.


To alleviate this issue, we apply an exponential decay to the flow values, and compute an interpolation between the new flow result and the previous one.

we update the flow at iteration τ as Algorithm 1 Iterative training X , Y: vocabularies, T : number of iterations, N : number of cognate pairs to identify.


Ablation study Finally, we investigate contribution of various components of the model architecture to the decipherment performance.

we look at the design choices directly in-System UGARITIC NeuroCipher 65.9 -monotonic 0.0 -residual 0.0 -flow 8.6 Table 5: Results for the noisy setting of UGARITIC.


To achieve this, we propose a novel graph matching method to estimate the similarity of two graphs.

we first utilize a graph convolutional neural network (GCN) (Kipf and Welling, 2016;Hamilton et al., 2017) to encode two graphs, say G 1 and G 2 , resulting in a list of entity embeddings for each graph.


Figure 2 gives an overview of our method for aligning Lebron James in the English and Chinese knowledge graph 1 .

we fist retrieve topic entity graphs of Lebron James from two KGs, namely G 1 and G 2 .


Then, we propose a graph matching model to estimate the probability that G 1 and G 2 are describing the same entity.

the matching model includes the following four layers: Input Representation Layer The goal of this layer is to learn embeddings for entities that occurred in topic entity graphs by using a GCN (henceforth GCN 1 ) (Xu et al., 2018a).


We propose an attentive-matching method similar to (Wang et al., 2017).

we first calculate the cosine similarities of entity e 1 i in G 1 with all entities {e 2 j } in G 2 in their representation space.


Training and Inference To train the model, we randomly construct 20 negative examples for each positive example <e 1 i , e 2 j > using a heuristic method.

we first generate rough entity embeddings for G 1 and G 2 by summing over the pretrained embeddings of words within each entity's surface form; then, we select 10 closest entities to e 1 i (or e 2 j ) in the rough embedding space to construct negative pairs with e 2 j (or e 1 i ).


The monolingual attention weights of YS attending to Y is relayed to form the teacher attention weights of YS attending to X.

y 2 and y 4 receive top-2 attention weights from yS 1 in Figure 2, and y 2 receives biggest attention from X 1 , y 4 receives biggest attention from X 2 .


To build the evaluation sets, English sentences of the validation and test sets of Gigaword and DUC2004 are manually translated into Chinese by graduate students of the linguistics department and our institute, who are bilingual with Chinese as the mother tongue.

in the Gigaword validation set, we randomly select 2000 sentence-summary pairs and manually translate their English sentences into Chinese.


Suppose two embedding matrices X and Z can be aligned with an orthogonal matrix W such that Z = WX.

let orthogonal mappings can only align embedding spaces with equal-magnitude centers.


We now develop Iterative Normalization, which transforms monolingual word embeddings to satisfy both length-invariance and center-invariance.

we normalize word embeddings to simultaneously have unit-length and zero-mean.


The task aims to find a linear transformation W so that for any source word embedding x, Wx lies close to the embedding y of its translation.

figure 2 presents the sketch of our proposed morphology-aware alignment model, which consists of a learnable linear transformation W and a parameter-fixed denoising evaluator E.  The word-to-word translation is accomplished by linear transformation W. for each word s i in a source sentence , it is translated by retrieving the nearest target word t i based on cosine 1 similarity.


The largescale JW300 corpus offers such supervision (i.e., seed translation pairs) for a large number of language pairs.

instead of resorting to fully unsupervised models for the language pairs included in JW300, we can use seed bilingual dictionaries from the parallel data to learn the projections.


Since these systems are complementary, future work could further explore the benefits of injecting the improved JW300 projections to more complex learners such as DSDS.

dSdS would likely benefit from better projections, since the ones that its current instance uses are inferior to JW300.


This makes it possible to specifically adapt to the language that we would like to analyze, both with respect to the cross-lingual word embeddings and the syntactic parameters of the model itself.

our approach builds on two previous works.


Across both POS tagging and dependency parsing tasks, we run experiments on Universal Dependency Treebanks (v2.2) (Nivre et al., 2018).

we train the proposed model on the English corpus with annotated data and fine-tune it on target languages in an unsupervised way.


(2019), on dependency parsing task we also use universal gold POS tags to index tag embeddings as part of observations.

the tag embeddings are concatenated with word embeddings to form x, tag embeddings are updated when training on the source language, and fixed at fine-tuning stage.


Therefore, we use two different sets of regularization parameters for nearby languages and distant languages, respectively.

β for the "distant languages" group is set as β 1 = β 2 = β 3 = 0.1, tuned on the Arabic development set, and for the "nearby languages" group β is set as β 1 = β 2 = β 3 = 1, tuned on the Spanish development set.


We have five input distance features: geographic, genetic, syntactic, inventory, and phonological.

we fit a generalized linear model (GLM) on the difference in accuracy and five features of all 20 target languages, then we perform a hypothesis test to compute the p-value that reflects the significance of specific features.


: Results in BLI of VECMAP, BIVEC, and SENTID BWE, on the "full" Muse bilingual lexicons, without filtering the vocabulary.

the compared BWE do not have the same vocabulary.


We then consider the following pattern-based methods:  SVD Count (sp) To account for missing relations, we also compare against low-rank embeddings of the Hearst corpus using Singular Value Decomposition (SVD).

let X ∈ R M xM , such that X ij = w(i, j)/W and U ΣV ⊤ be the singular value decomposition of X, then: We also evaluate against the SVD of the PPMI matrix, which is identical to sp(i, j), with the exception that X ij = ppmi(i, j), instead of p(i, j).


Firstly, word data sparsity inevitably leads to overfitting and the ubiquity of OOV words limits the model's learning capacity.

zipf's law applies to most languages including Chinese.


Secondly, the state-of-the-art word segmentation performance is far from perfect, the errors of which would bias downstream NLP tasks.

cWS is a relatively hard and complicated task, primarily because word boundary of chinese words is usually quite vague.


The squared Euclidean distance between x and y is a decreasing linear function of csPMI(x, y).

the more similar two words are (as measured by csPMI) the smaller the distance between their vectors.


However, we do not need the word embeddings themselves to verify coplanarity; when there is no reconstruction error, we can express it as a constraint over M, the matrix that is implicitly factorized by the embedding model (see Definition 5).

lemma 2 A linear analogy f : x → x + r holds over a set of ordered pairs S in an SGNS or GloVe word embedding space W with no reconstruction error iff ∃ λ ∈ R, g : x c → x c + λ r holds over S in the corresponding context space C. an analogy f that holds over S in the word space has a corresponding analogy g that holds over S in the context space.


We introduce a novel technique to blend hierarchical information with distributional information for predicting compositionality.

we use hypernymy information of the multiword and its constituents encoded in the form of the recently introduced Poincaré embeddings in addition to the distributional information to detect compositionality for noun phrases.


We evaluate our proposed metric on three well-accepted English datasets, i.e., Reddy (Reddy et al., 2011), Reddy++  and , demonstrating a performance boost when including hyperbolic embeddings by 2-4% absolute points across all datasets.

our work contains the three following contributions: 1.


The concept embeddings are trained in a similar way as name embeddings.

for this baseline, we utilize a pre-annotated corpus where names appearing in the training text are labeled with their associated concepts.


As mentioned in the introduction, training the encoder using only this synonym-based objective will lead to biased representations.

the encoder will be trained to act like a hash function, which performs well on determining whether two names are synonym of each other.


To make relational information available to NLP models, it then suffices to use a standard architecture and replace normal word vectors by concatenations of standard and relational word vectors.

we show that such relational word vectors can be learned directly from a given set of relation vectors.


Despite the fact that such methods learn word vectors from which relation vectors can be predicted, it is unclear to what extent these word vectors themselves capture relational knowledge.

the aforementioned methods have thus far only been evaluated in settings that rely on the predicted relation vectors.


(2015a) by using a smoothed version of pointwise mutual information (PMI), where we use 0.5 as exponent factor.

for each word w ∈ V, the set R contains all sufficiently frequently co-occurring pairs (w, v) for which v is within the top-100 most closely related words to w, according to the following score: where n wv is the harmonically weighted 3 number of times the words w and v occur in the same sentence within a distance of at most 10 words, and: This smoothed variant of PMI has the advantage of being less biased towards infrequent (and thus typically less informative) words.


Word vector averaging has moreover proven surprisingly effective for learning relation vectors (Weston et al., 2013;Hashimoto et al., 2015;Fan et al., 2015; Espinosa Anke and Schockaert, 2018), as well as in related tasks such as sentence embedding (Wieting et al., 2016).

to construct the relation vector r wv capturing the relationship between the words w and v we proceed as follows.


However, it should be noted that relational word vectors behave differently from standard word vectors, and we should not expect the relational word vectors to be meaningful in unsupervised tasks such as semantic relatedness (Turney and Pantel, 2010).

note that a high similarity between e w and e v should mean that relationships which hold for w have a high probability of holding for v as well.


Table 4 shows, for a small number of selected word pairs, the top nearest neighbors that were unique to our 300-dimensional relational word vectors.

these pairs were not found among the top 50 nearest neighbors for the Fast-Text word vectors of the same dimensionality, using the standard vector difference encoding.


In order to define and substantiate our approach we introduce an axiomatic characterization of antonymy and synonymy based on the work done by Edmundson (1967).

synonymy and antonymy are modeled as relations and a set of axioms is proposed.


In this paper, however, we will use priors on the parameters of the word embedding model itself.

we will impose a prior on the context word vectorsw, i.e.


Next we describe our document retrieval experiments.

we consider this problem as a learning-to-rank (LTR) task and use standard information retrieval (IR) tools to present our evaluation results.


Our training strategy is mostly the same as for the document categorization experiments, although for some parameters, such as the number of topics and vMF mixture components, we used larger values, which is a reflection of the fact that the collections used in this experiment are substantially larger and tend to be more diverse (Wei and Croft, 2006).

the word vector lengths were chosen from a pool of {150, 200, 300} and the vMF mixtures from a pool of {300, 1000, 3000}.


Classic distributional semantics attempted to do composition to account for contextual effects, but it was in general unable to go beyond short phrases (Baroni, 2013); newer-generation neural network models have supposed a big step forward, as they can natively do composition (Westera and Boleda, 2019).

the hidden layer activations in an RNN can be seen as putting words in context, as they combine the word embedding with information coming from the context (the adjacent hidden states).


Grounded information has been extensively used in various NLP tasks to represent real-world knowledge (Niles and Pease, 2003;Gruber, 1993;Guarino, 1998;de Bruijn et al., 2006;Paulheim, 2017) .

early question answering systems used expert-crafted ontologies in order to endow these systems with common knowledge (Harabagiu et al., 2005;Xu et al., 2016).


We evaluate our model on SemEval 2018 Task 9, the only existing benchmark for the hypernym discovery task.

we focus on the 3 English sub-tasks: general English, Medical literature, and Music literature.


To avoid such cases, we propose to evaluate a candidate's fitness by comparing the sentence's contextualized representation before and after the substitution for validation.

for a word x i , we use the concatenation of its representations in top four layers in BERT as its contextualized representation.


This paper presents Decomposable Neural Paraphrase Generator (DNPG), a Transformer-based model that can learn and generate paraphrases of a sentence at different levels of granularity in a disentangled way.

the model is composed of multiple encoders and decoders with different structures, each of which corresponds to a specific granularity.


For instance, the following pair of paraphrases contains both the phrase-level and the sentence-level patterns.

what is the reason of $x → what makes $x happen world war II → the second world war the blue part is the sentence-level pattern, which can be expressed as a pair of sentence templates, where $x can be any fragment of text.


To address this phenomenon in paraphrase generation, we propose Decomposable Neural Paraphrase Generator (DNPG).

the DNPG consists of a separator, multiple encoders and decoders, and an aggregator.


We employ the Transformer architecture for the encoders and decoders in DNPG.

the phrase-level Transformer is composed of m-encoder 0 and m-decoder 0 , which is responsible for capturing the local paraphrasing patterns.


For the sentence-level Transformer, in order to make the positional encoding insensitive to the lengths of the phraselevel fragment, we set: Multi-head Attention: We modify the selfattention mechanism in the encoders and decoders by setting a different receptive field for each granularity, as illustrated in Figure 2.

for the phrase-level model, we restrict each position in the encoder and decoder to attend only the adjacent n words (n = 3), so as to mainly capture the local composition.


For a fair comparison, we use the Transformer model with similar number of parameters with our model.

it is with 3 layers, model size of 450 dimensions, and attention with 9 heads.


Table 4 shows the performances of the models, indicating that DNPG achieves competitive performance in terms of all the automatic metrics among all the models.

the DNPG has similar performance with the vanilla Transformer model on Quora dataset, while significantly performs better on WikiAnswers.


By using the predicted granularity levels and the context attentions in the Transformer, we are able to extract the phrasal and sentential paraphrase patterns from the model.

we extract the sentential templatesX  of X (orȲ of Y ) by substituting each fragment of words at the phrasal level by a unique placeholder such as $x.


We pick up the one with the best accuracy and readability.

we re-rank them by P (Ŷ b |X, Z) calculated by the full model of DNPG.


However, the language model of the source and target domains may differ, we therefore finetune the separator of DNPG so that it can identify the granularity of the sentence in the target domain more accurately.

to adapt the separator P sep (Z|X) to the target domain, we employ a reinforcement learning (RL) approach by maxi-mizing the accumulative reward: We define the reward functions based on the principle that the source and target domain share the similar sentence templates.


We quantify the performance gain of each inductive bias we incorporated in the DNPG model.

we compare the DNPG with three variants: one with vanilla attention modules, one with vanilla positional encoding and the one uses vanilla softmax.


Please evaluate the paraphrase with respect to three criterions: readability, accuracy, and diversity, which are arranged by importance.

the criterions of paraphrase quality from bad to good are listed in detailed as following: 1.


Please note that readable is not equivalent to grammatical correct.

considered there are non-English speaker, a readable paraphrase can have grammar mistakes.


For this purpose, we cover a wider range of syntactic constructs (10 in total) than state-of-the-art rule-based syntactic frameworks.

our approach is not limited to breaking up clausal components, but also splits and rephrases a variety of phrasal elements, resulting in a much more fine-grained output where each proposition represents a minimal semantic unit that is typically composed of a simple subject-predicate-object structure.


Some heuristics are special cases of others, but we made sure that the examples for one heuristic did not also fall under a more narrowly defined heuristic.

for lexical overlap cases, the hypothesis was not a subsequence or constituent of the premise; for subsequence cases, the hypothesis was not a constituent of the premise.


All models behaved as we would expect them to if they had adopted the heuristics targeted by HANS.

they nearly always predicted entailment for the examples in HANS, leading to near-perfect accuracy when the true label is entailment, and near-zero accuracy when the true label is non-entailment.


In terms of data scale, the LSTM LM trained on the full training set substantially outperforms the LSTM trained on the smaller training set.

the performance difference between the small and full LSTMs sheds light on which constructions are sensitive to variations in the amount of data.


For efficiency, we approximate this using the onebest predicted tree from a pre-trained Berkeley parser, 8 denoted asŷ berk (x * ), as follows: whereŷ berk <j (x * ) are all the non-terminals in y berk (x * ) that occur before x * j .

we first parse the sentence with a Berkeley parser, and use the resulting tree prefix as conditioning context to compute the probability of generating w ∈ Σ under the RNNG.


To address this problem, we combine the PRPN with a parser which explicitly models tree-building operations.

we use the pyramid-shaped, tree-based long short-term memory (Tree-LSTM, Figure 1a, Choi et al., 2018), where reinforcement learning (RL) in this model can be relaxed by Gumbel-Softmax.


Our results show the usefulness of a downstream task for unsupervised parsing.

policy refinement with a semantically oriented objective improves parsing performance by two F -score points, outperforming the previous state-of-the-art PRPN model.


The input is the embeddings of the current word and its left context The output is given by: In fact, absolute distance values are not required, it is sufficient to preserve their order.

if   Predict next word Gated-weighted attention Figure 3: The prediction of the next word in the PRPN language model.


It is used in the training of the Tree-LSTM model (Choi et al., 2018), as well as policy refinement in our imitation learning.

we use the straight-through Gumbel-Softmax (ST-Gumbel, Jang et al., 2017).


For relation extraction, for example, GCNs have been shown superior to tree LSTMs for encoding a dependency tree (Zhang et al., 2018c) We investigate both GCNs and GRNs as tree communication models for tree sentiment classification.

initialized with a vanilla tree LSTM representation, each node repeatedly exchanges information with its neighbours using graph neural networks.


(2017) extend the use of graph convolutional network (GCN) to NLP tasks.

they use GCN to learn dependency-syntactic word representation for semantic role labeling and machine translation, respectively.


Formally, a tree-LSTM calculates a cell state through an input gate, an output gate and two forget gates at each time step.

at time step t, the input gate i t and the output gate o t are calculated respectively as follows: co and b o are parameters of the input gate and the output gate, respectively.


Finally, the hidden state h t of the current node is calculated based on the current cell c t and the output gate o t : Limitation Tree-LSTM models capture only bottom-up node dependencies.

for a node j, the hidden representation h tree j is dependent on the descendant nodes only.


Figure 3: Recurrent tree communication model.

given a constituent tree, for each constituent node j, the initial state h ′ j is obtained using a tree-LSTM: where h ′ j is the hidden state of the node j, c ′ j is the cell state of node j, left(j) denote the left child of node j, right(j) denotes the right child of node j.


We apply the strategy of , where multiple convolutional layers can be used for information combination.

for the k-th layer, transformed inputs are obtained by linear transformation for each channel: where W k,e and b e (e ∈ {self, up, down}) are model parameters, and h k−1,e j (e ∈ {self, left, right, down}) is the hidden state of last layer for node e(j).


For each recurrent step t, the hidden states from the last recurrent step are taken to calculate the cell state of the current state.

for node j, the hidden state of the previous step can be divided into the last hidden state h self j from self-to-self channel, the last hidden state h up t−1,j from bottom-up channel and the last hidden state h down t−1,j from the top-down channel: We calculate gate and state values based on the inputs and last hidden states from the three information channels.


Compared with previous methods based on constituent tree-LSTM, our model improves the preformance on different datasets and settings.

it outperforms BiConL-STM (Teng and Zhang, 2017), which use bidirectional tree-LSTM.


Therefore, we apply a constituency parser to the source sentence.

we use the Stanford CoreNLP shift-reduce parser (Zhu et al., 2013) to generate a parse tree 1 .


This new task aims to predict sentiment polarities for specific aspects from interactive QA style reviews.

a high-quality annotated corpus is constructed for ASC-QA to facilitate corresponding research.


In this paper, we propose a reinforced bidirectional attention network approach to tackle the above two challenges.

we first propose a word selection model, namely Reinforced Aspect-relevant Word Selector (RAWS), to alleviate the effects of noisy words for a specific aspect through discarding noisy words and only select aspect-relevant words in a word sequence.


Since corpus annotation is labor-expensive and time-consuming, we randomly select 10k QA text pairs from each domain to perform annotation.


In order to mine semantic matching information between question and answer, we employ S to compute attentions in both directions, which could be seen as a Question-to-Answer attention and an Answer-to-Question attention.

we first employ the row/column-wise softmax operation to get two normalized matrices S r and S c .


The other hyper-parameters are tuned according to the development set.

we adopt Adam optimizer (Kingma and Ba, 2014) with an initial learning rate of 0.01 for crossentropy training and adopt the SGD optimizer with a learning rate of 0.002 for all policy gradients training.


We provide a qualitative analysis of our approach on the development set.

in Figure 4, we visualize the attention matrix S r in RBAN towards aspect "operating speed" in two cases, i.e., not using RAWS (Figure 4(a)) and using RAWS (Figure 4(b)).


In this paper, we propose a new task, i.e., Aspect Sentiment Classification towards Question Answering (ASC-QA).

we first build a high-quality human annotated benchmark corpus.


Instead, we use web data provided by Common Crawl.

4 we consider each of the individual pages in the July 2018 archive (roughly one per URL) as a single document.


Finally, ROUGE-20% measures the model's ability to complete an answer given the first 80% of the reference answer, the question, and the support document.

we generate a number of tokens corresponding to 20% of the average answer length in the validation set, and measure ROUGE between these and the last 20% of the reference.


We report ROUGE for a retrieval system that returns the answer of the closest question in the training set.

we perform a nearest neighbor search (Johnson et al., 2017) over the average word embeddings of the question using FAST-TEXT (Bojanowski et al., 2017).


While we exploit the same architecture described in the previous section, we have reversed the role of the candidate answer and the contexts in (1) as a self-supervised one.

we set the problem as inferring the Top-1 context for the chosen answer candidate.


In fact, we observe that appropriate captions can be very useful for many VQA questions.

we trained a model to answer visual questions for the VQA v2 challenge (Antol et al., 2015) only using the human annotated Human Captions : 1) A man on a blue surfboard on top of some rough water.


In order to encourage the generation of relevant captions, we propose a novel greedy algorithm that aims to minimize the cross entropy loss only for the most relevant and helpful gold-standard captions.

helpfulness is measured using the inner-product of the gradients from the caption generation loss and the VQA answer prediction loss.


Recently, a large amount of attention-based deeplearning methods have been proposed for VQA, including top-down (Ren et al., 2015a;Fukui et al., 2016;Wu et al., 2016;Goyal et al., 2017;Li et al., 2018a) and bottom-up attention methods (Anderson et al., 2018;Li et al., 2018b;Wu and Mooney, 2019).

a typical model first extracts image features using a pre-trained CNN, and then trains an RNN to encode the question, using an attention mechanism to focus on specific features of the image.


We use object detection as bottom-up attention (Anderson et al., 2018), which provides salient image regions with clear boundaries.

we use a Faster R-CNN head (Ren et al., 2015b) in conjunction with a ResNet-101 base network (He et al., 2016) as our detection module.


Then, we design a caption attention module A c which utilizes the question-attended feature set V q , question features q, and h 1 i,t to generate the attention weight on the current word in order to indicate its importance.

the Word GRU first encodes the words embedding ⇧ c i,t in Eq.


11: We frame the answer prediction task as a multilabel regression problem (Anderson et al., 2018).

we use the soft scores in the goldstandard VQA-v2 data (which are used in the evaluation metric), as labels to supervise the sigmoidnormalized predictions as shown in Eq.


The key difference between our module and theirs lies in the input features and the caption supervision.

we use the question-attended image features V q as inputs, and only use the most relevant caption, which is automatically determined in an online fashion (detailed below), for each question-image pair to train the captioning module.


After that, we generate question-relevant captions for all question-image pairs in the COCO train, validation, and test sets.

we sample 5 captions per questionimage pair.


2018, we perform standard text pre-processing and tokenization.

questions are first converted to lower case and then trimmed to a maximum of 14 words, and the words that appear less than 5 times are replaced with an "<unk>" token.


2.06%, which indicates the effectiveness of including caption features as additional inputs.

we observe that our single model outperforms other methods, especially in the 'Num' and 'Other' categories.


Next, we analyze the difference between using automatically generated captions and using those provided by human annotators.

we train our model with generated question-agnostic captions using the Up-Down (Anderson et al., 2018) captioner, question-relevant captions from our caption generation module, and human annotated captions from the COCO dataset.


fectiveness of utilizing captions to adjust the topdown attention weights, in addition to the advantage of providing additional information.

we compare our model with a baseline version where the top-down attention-weight adjustment factor A cv is manually set to 1.0 (resulting in no adjustment).


The VQA-X dataset contains 2, 000 question-image pairs from the VQA v2 validation set with human annotations indicating the objects which most influence the answer to the question.

we used Earth Mover Distance (EMD) (Rubner et al., 2000) to compare the highly-attended objects in the VQA process to the objects highlighted by human judges.


In this work, we have explored how generating question-relevant image captions can improve VQA performance.

we present a model which jointly generates question-related captions and uses them to provide additional information to aid VQA.


This approach only utilizes existing image-caption datasets, automatically determining which captions are relevant to a given question.

we design the training algorithm to only update the network parameters in the optimization process when the caption generation and VQA tasks agree on the direction of change.


In order to identify related objects more precisely, this paper proposes a multi-grained attention mechanism that involves object-level grounding complementary to the sentence-image association.

a matching model is trained on an object-detection dataset to learn explicit correspondence between the content words in the question and their visual counterparts.


We adopt the object-detection-based approach to represent the input image.


A mean opinion score (MOS) test to quantitatively compare our attention mechanism with the baseline model is also performed.

we randomly select 100 cases and generate their attention maps.


Contributions: Our study contributes to the literature on CL in NLP.

: i) we introduce a CL setup based on linguistically-informed task pairs which differ with respect to question types and level of difficulty; ii) we show the importance of task order, an often overlooked aspect, and observe asymmetric synergetic effects; iii) our results show that our VQA model suffers from extreme forgetting; rehearsal gives better results than a regularization-based method.


However, in the proposed method, no matter whether the similar source and target words are paired or not, they tend to cluster together; as shown in Figure 6(b) and 6(c).

the proposed method is able to handle the challenge of synonym.


Due to the specific domain we are annotating (English language fiction), we have also found it necessary to define several rules that are not explicitly presented in the ACE or Light ERE standards.

since mental states play an especially prominent and complex role in many novels (and noticeably so relative to more fact-based discourses such as the news) we have given particular attention to defining rules for stative events.


To this end, we propose a novel word reordering detection task to quantify how well the word order information learned by SAN and RNN.

we randomly move one word to another position, and examine whether a trained model can detect both the original and inserted positions.


To demonstrate the universality of the findings in this study, we also conduct experiments on WAT17 English⇒Japanese (En⇒Ja) data.


Yet, the lived reality on social media platforms falls far short of this potential (Papacharissi, 2004).

the promise of social media has been hindered by antisocial, abusive behaviors such as harassment, hate speech, trolling, and the like.


There are other, somewhat less related, ways to use feedback during dialogue for learning, notably for collecting knowledge to answer questions (Mazumder et al., 2018;Hixon et al., 2015;Pappu and Rudnicky, 2013), and more commonly in reinforcement learning settings, where the feedback is a scalar rather than the dialogue messages themselves (Levin et al., 2000;Schatzmann et al., 2006;Rieser and Lemon, 2011;Hong et al., 2019).

(Serban et al., 2017) employ user sentiment detection for reward shaping in their Alexa prize entry.


It is important to note that the user's responses are always in the form of natural dialogue.

at no point are the new FEEDBACK examples inspected, post-processed, or cleaned.


The architecture of EmoDS is shown in Figure 1.

we use bidirectional long-short term memory (LSTM) (Hochreiter and Schmidhuber, 1997;Schuster and Paliwal, 1997) as an encoder to transform a post, into its vector representation.


(2016), we use the idea of expected word embedding to approximate Q(E|Y ).

the expected word embedding is a weighted sum of embeddings of all the possible words at each time step: where for each time step j, we enumerate all possible words that are in the union of V g and V z e .


We implemented our EmoDS in Tensorflow 5 .

we used one layer of bidirectional LSTM for encoder and another uni-directional LSTM for decoder, with the size of LSTM hidden state set as 256 in both the encoder and decoder.


It is shown in Table 6 that EmoDS achieved the highest performance in most cases (Sign Test, with p-value < 0.05).

for content coherence, there was no obvious difference among most models, but for emotional expression, the EmoDS yielded a significant performance boost.


In order to alleviate such issue, we propose a hierarchical graph representation by leveraging the structural property of dialog acts.

we first build a multi-layer tree to represent the entire dialog act space based on their interrelationships.


In Figure 2, we show how the dialog act graph structure is explicitly encoded into model architecture.

hDSA consists of multiple layers of disentangled self-attention modules (DSA).


Experiment results on the large-scale Multi-WOZ dataset (Budzianowski et al., 2018) show that our HDSA significantly outperforms other competing algorithms.

1 the proposed hierarchical dialog act representation effectively improves the generalization ability on the unseen test cases and decreases the sample complexity on seen cases.


We first explain the utterance encoder module, which uses a neural network f ACT to encode the dialog history (i.e., concatenation of previous utterances from both the user and the system turns where The overall featureū is used to predict the hierarchical representation of dialog act.

we output a vector P θ (A) ∈ R H 0 , whose i th component gives the probability of the i th node in the dialog act graph being activated: where V a ∈ R D×H 0 is the attention matrix, the weights W u , W b , b are the learnable parameters to project the input to R D space, and σ is the Sigmoid function.


The main innovation of our architecture lies in disentangling the heads.

instead of concatenating G i to obtain the layer output like the standard Transformer, we employ a binary switch vector s = (α 1 , .


, α H ) ∈ {0, 1} H to control H different heads and aggregate them as a n × D output matrix G = H i=1 α i G i .

the j-th row of G, denoted as C j ∈ R D , can be understood as the output corresponding to the j-th input token y j in the response.


Generalization Ability To better understand the performance gain of the hierarchical graph-based representation, we design synthetic tests to examine its generalization ability.

we divide the dialog acts into five categories based on their frequency of appearance in the training data: very few shot (1-100 times), few shot (100-500 times), medium shot (500-2K times), many shot (2K-5K times), and very many shot (5K+ times).


To address this problem, we propose a novel incremental learning framework to design task-oriented dialogue systems, or for short Incremental Dialogue System (IDS), without pre-defining the exhaustive list of user needs.

we introduce an uncertainty estimation module to evaluate the confidence of giving correct responses.


In this paradigm, all reasonable queries related to the current task are legal, and the system can learn to deal with user queries online.

after the user sends a query to our system, we use an uncertainty estimation module to evaluate the confidence that the dialogue model can respond correctly.


Based on such intuitive analysis, we design the uncertainty measurement for IDS.

we assume that the latent variable z obeys a multivariate diagonal Gaussian distribution.


Thus, we add an additional criterion for the uncertainty measurement.

if the maximum probability in P avg is lower than a threshold τ 2 , the system will refuse to respond.


To simulate the new unconsidered user needs, we train the dialogue system on a smaller subset and test it on a more complicated one.

our dataset contains five different subsets within the context of customer services.


• IDS − : IDS without updating model parameters during testing.

iDS − is trained only with human intervention data on the training set and then we freeze parameters.


Because IDS requires human intervention to reduce risks whenever there is low confidence, we calculate the average turn accuracy only if IDS chooses to respond without human intervention.

compared with baselines, IDS computes the turn accuracy only on a subset of test sets.


In contrast, our work makes use of the Bayesian nature of variational inference to estimate the uncertainty and learn from humans.

we sample variables from the prior network as the random perturbation to estimate the model uncertainty following the idea of Query-By-Committee (Seung et al., 1992) and optimize model parameters by maximizing the ELBO.


The motivation comes from the fact that self-attention is superior in capturing long distant dependency, as shown in (Vaswani et al., 2017).

we first use a word-level LSTM encoder to obtain the fixed-dimensional representation of each context.


(Tian et al., 2017) proposed a weighted sequence (WSeq) attention model for HRED, using the cosine similarity to measure the degree of the relevance.

they first calculate the cosine similarity between the post embedding and each context sentence embedding, and then use this normalized similarity score as the attention weight.


For JDC, we utilize the Chinese word as input.

we use the Jieba tool for word segmentation, and set the vocabulary size as 69,644.


We use both quantitative metrics and human judgements for evaluation in our experiment.

we use two kinds of metrics for quantitative comparisons.


To verify whether the performance improvements are owing to the detected relevant contexts, we conduct a further data analysis, including both quantitative evaluation and case study.

we randomly sample 500 context-response pairs from the JDC dataset, denoted as JDC-RCD 4 .


Since HRED considers all the context as relevant context, we calculate the error rate for evaluation.

one minus the proportion of all-contextrelevant in the JDC-RCD data, i.e.


Reducing Dialogue Consistency to NLI Identifying utterances which contradict previous utterances or an agent's persona can be reduced to natural language inference by assuming that contradictions are contained in a sentence pair.

given a persona P A = {p A 1 , ..., p A m } for agent A and a length- T , it is assumed that a dialogue contradiction for agent A is contained in an utterance pair (u A i , u A j ), and a persona contradiction is contained in a pair (u A i , p A k ).


If the NLI model predicts that a candidate contradicts a persona sentence, the candidate's score is penalized, with the penalty weighted by the NLI model's confidence 5 scaled by a constant.

assume a dialogue model Given a persona P = {p 1 , ..., p m }, previous utterances u ≤t , and a set of candidate nextutterances U , the dialogue model outputs a ranked list of scores s 1 , s 2 , ..., s |U | corresponding to next-utterance candidates u 1 , u 2 , ..., u |U | .


The NLI model is then run on each (u i , p j ) pair, predicting a label y i,j ∈ {E, N, C} with confidence c i,j .

a contradiction score is computed for each candidate as: if the candidate u i does not contradict any persona sentence p j according to the NLI  is the highest confidence, c i,j , out of the contradicting (u i , p j ).


However, there is also room for improvement.

we report the performance of a model which takes the ground-truth triples as input instead of sentences.


The dialogue model must avoid ranking a contradicting candidate highly.

suppose the ground-truth nextutterance u t+1 is associated with triple (e 1 , r, e 2 ), e.g., (i, have pet, dog).


In reality, we often need to develop a dialogue agent with some fixed, limited budget due to limited project funding, conversational data, and development time.

in this study we measure budget in terms of the number of real user interactions.


Specifically, in this study we measure budget in terms of the number of real user interactions.

we strive to optimize a dialogue agent via a fixed, small number of interactions with real users.


In this manner, the whole allocation process essentially is a discrete stochastic process, which can be modeled as a Poisson process.

at each training step k, the probability distribution of a random variable b k equaling n is given by: The global scheduling in BCS is based on a Decayed Possion Process, motivated by two considerations: (1) For simplicity, we assume that all budget allocations are mutually-independent.


Here we employ the Deep Qnetwork (DQN) (Mnih et al., 2015).

at each step, the agent observes the dialogue state s, then chooses an action a using an -greedy policy that selects a random action with probability , and otherwise follows the greedy policy a = arg max a Q(s, a ; θ Q ).


As shown in Figure 3, the BCS-DDQ agent consistently outperforms other baseline agents by a statistically significant margin.

when the budget is small (b = 50), SL does better than DQN and DDQ that haven't been trained long enough to obtain significant positive reward.


2019proposed a clustering-based beam search method to help condense and remove meaningless responses from chatbots.

at each decoding step t, this method initially considers the top 2 * b candidates.


To tackle the above two problems, we present a Vocabulary Pyramid Network (VPN) which is able to incorporate multi-pass encoding and decoding with multi-level vocabularies into response generation.

the dialogue input and output are represented by multi-level vocabularies which are obtained from hierarchical clustering of raw words.


In this study, we present Vocabulary Pyramid Networks (VPN) to tackle the issues of one fixed vocabulary and one-pass decoding simultaneously.

vPN incorporates multipass encoding and decoding with multi-level vocabularies into response generation.


In the secondpass decoder, an extra attention model is leveraged to selectively read the output vector sequence Y 1 from the first-pass decoder, and then generate the refined output sequence  3 Methodology As shown in Figure 2, VPN consists of three submodules: multi-level vocabularies (Section 3.2), multi-pass encoder (Section 3.3) and multi-pass decoder (Section 3.4).

multi-level vocabularies contain raw words, low-level clusters and high-level clusters (black, blue and red solid rectangles in Figure 2).


As illustrated in Figure 3, multi-level vocabularies contain three different vocabularies: raw words, low-level clusters and high-level clusters.

the raw words are the original words in the training data, and they are denoted as .., w h H } by "bottom-up" hierarchical clustering.


In order to decide which clusters could be agglomerated, we utilize the implementation of hierarchical clustering in Scipy 3 .

we pre-train rawword embeddings by the word2vec model 4 as inputs, and then we leverage the Ward (Ward, 1963) linkage and maxclust 5 criterion to automatically construct hierarchical clustering.


The concatenated representation of bi-directional hidden states: , is regarded as the encoded vector for each input word.

finally, the input sequence is transformed into a hidden state sequence: the initiated hidden state is a zero vector, and the hidden state (h r L i ) in the last word could be used for initiating the next encoder (lowlevel encoder).


The total losses are minimized by an Adam optimizer (Kingma and Ba, 2015) with 0.0001 learning rate.

the size of lowlevel clusters and high-level clusters are 3400 and 340, respectively, which are significantly smaller than the size of raw words (34000), and these clusters are also represented by 300-dimensional vectors.


Evaluations on Twitter are based on token level.

the BLEU and ROUGE on Weibo dataset are based on the Chinese character because Chinese characters are with semantics.


We can clearly see that removing any encoder and decoder causes obvious performance degradation.

"w/o highlevel ED" obtains worse performances than "w/o low-level ED".


Based on this knowledge graph, we create a new knowledge-driven conversation dataset, namely the Baidu Conversation Corpus (DuConv) to facilitate the development of proactive conversation models.

duConv has around 30k multi-turn conversations and each dialog in the duConv is created by two crowdsourced workers, where one plays the role of the conversation leader and the other one acts as the conversation follower.


Given the knowledge graph, we sample some knowledge paths, which are used as conversation goals.

we focus on the simple but challenging scenario: naturally shifting the topics twice, i.e., from "[start]" state to "topic a" then finally to "topic b".


We use the BOW loss proposed by , to ensure the accuracy of the fused knowledge k c by enforcing the relevancy between the knowledge and the true response.

let w = MLP(k c ) ∈ R |V | , where |V | is the vocabulary size and we define: Then, the BOW loss is defined to minimize: In summary, the final loss of our generative model is: 5 Experiments Our proposed models are tested under two settings: 1) automatic evaluation and 2) human evaluation.


Such methods are called retrieval-augmented generative models and their objectives are to maximize P (r|q, R), where R is one or a few (at most 3 in practice) retrieved results.

some researchers (Li et al., 2017;Zhuang et al., 2017;Song et al., 2018) build the combination of retrieval and generative models, which retrieve one or a few responses r + , and then feeds both the query q and r + into the generative model to maximize P (r|q, R = r + ).


The advantage is that our model is less sensitive to the quality of individual q-r pairs and hence increases the robustness of response generation.

we divide the training corpus into multiple groups by clustering, extract common characteristics of each group, and learn to utilize the characteristics to assist generation.


To achieve this, different from conventional methods which view the multiple responses as independent ones, we propose to consider the correlation of multiple responses with a novel twostep generation architecture, where the response bag {y} and each response y ∈ {y} are modeled by two separate features which are obtained in each generation phase respectively.

we assume a variable c ∈ R n representing the common feature of different responses and an unobserved latent variable z ∈ Z corresponding to the distinct feature for each y in the bag.


Secondly, the prior common feature c can provide extra information for the sampling network which is supposed to decrease the latent searching space.

similar to the CVAE architecture, the overall objective for our model in the second generation phase is as below: where q φ represents the recognition network and p ϕ is the prior network with φ and ϕ as the trainable parameters; D(•||•) is the regularization term which measures the distance between the two distributions.


However, it differs in that it factors the conditional distribution to word-level in an auto-regressive manner.

let the word tokens in u T be [w 1 , ...w N ].


The replacement procedure is the same as the one described for MUR, where a uniform random index t is selected in the dialog context and u t is replaced by a negative sample q.

while MUR strives to create a model that finds the original utterance, given the replacement index t, InI aims to train a model that can identify the replacement position t. this is done via:û Finally, the loss function is to minimize the cross entropy of the replaced index: This pretraining objective aims to explicitly model the coherence of the dialog, which encourages both local representations of each individual utterance and a global representation of the dialog context.


We also analyze prior disentanglement work.


Based on above triple sampling strategies, we define the task of inconsistent order detection as: given a dialogue history and the target utterance pair (Q t , A t ) for evaluation, the model needs to predict whether the sampled triple is misordered (output: 1).

1 e.g., for the added padding utterance Q−2, it is represented as a sequence of one same padding word {w where N is the roundedup averaged length of utterances in the dataset.


1 With the recent success of deep learning in NLP, however, there has been renewed interest in exploring the extent to which neural networks capture human behaviour in psycholinguistic tasks (e.g., Linzen and Leonard, 2018;Linzen, 2019).

kirov and Cotterell (2018;henceforth k&C) revisited the past tense debate and showed that modern sequence-based encoder-decoder (ED) models overcome many of the criticisms levelled at Rumelhart and McClelland's model.


In particular, Kirov and Cotterell (2018;henceforth K&C) revisited the past tense debate and showed that modern sequence-based encoder-decoder (ED) models overcome many of the criticisms levelled at Rumelhart and McClelland's model.

these models permit variable-length input and output that represent sequential ordering; can reach near-perfect accuracy on both regular and irregular verbs seen in training; and (using multi-task learning) can effectively generalize phonological rules across different inflections.


We explore the model's behaviour on this task in detail, and conclude that its ability to model humans is considerably weaker than K&C suggest.

we begin by showing that multiple simulations of the same model (with different random initializations) result in very different correlations with the human data.


The regulars and irregulars are treated separately.

the irregular correlation value is computed by considering the average human production probability (or rating) for each suggested irregular past tense, and comparing these with the model scores for those same forms.


In this paper, we are building on top of the psycholinguistic findings that words are recognized faster if preceded by words related in meaning (semantic priming) (Gulan and Valerjev, 2010), and we adopt spreading activation theory as one of the main theories that tries to explain how priming occurs.

we introduce a framework that considers sequential text reading and models two simultaneous processes: (i) a spreading activation process that runs over long-term memory (approximated by the knowledge graph), activates concepts and transfers them to working memory, and (ii) a process that tracks concepts and their activation in working memory and subjects them to forgetting.


We therefore hypothesize that in simpler texts, target concepts already exist in WM before they are explicitly mentioned in the text.

the higher CA(c) at the encounter of concept c, the easier it is to comprehend the concept c and connect it to its context.


The experimental re-sults show that both of our models perform better than the state-of-the-art baseline (Gao et al., 2018) across three benchmark datasets.

our MIP based model with a simple DNN architecture, outperforms the baseline with an average of 2.2% improvement in F1 score, whereas the SPV based model with a novel multi-head contextual attention mechanism achieves an even higher gain of 2.5% against the baseline.


We find that there are configurations that achieve good performance across the splits.

the best overall configuration, found by minimizing ranks across splits, has 0.01 learning rate, 25-tokens batch size, 0.25 dropout, 6 layers, 512 layer dimensionality, and kernels of width 5.


Indeed, logical implications are a special case of conditional probabilities that only take the values 0 and 1, rather than values in [0, 1].

we argue that probabilistic implications should take the form of the following conditional probability distribution: where π is a subvector that omits the indices i and j.


Is the rank ordering of algorithm performance similar across languages?

is it the case that the same algorithms perform poorly or well across languages?


Measuring embedding depth We approximate embedding relations through dependency graphs.

for our purposes we define embedding as any dependency such that (i) the dependent is the head of a clause and (ii) permuting head and dependent would lead to an ungrammatical sentence, unlike in "flat" syntactic structures such as coordinated clauses.


corresponds to a non-linear transfor-   In this work, we augment our models with existing linguistic and affective knowledge from human experts.

we leverage lexica containing psycho-linguistic, sentiment and emotion annotations.


1(b), learns a feature mask, which is applied on each word annotation h i .

a gate mechanism with a sigmoid activation function, generates a mask-vector from each c(w i ) with values between 0 and 1 (black and white dots in Fig.


(2017) and applies a featurewise affine transformation to the latent space of the hidden states.

we use the lexicon features c(w i ), in order to conditionally generate the corresponding scaling γ(•) and shifting β(•) vectors.


We now explore a more challenging setup for our model whereby instead of training and testing on edits of a specific annotated category, we train on edits of pages of a particular category but test on a previously unseen (during training) page of the same category.

for a given category, we train our model on 90% of pages and test our models on unseen page edits in the same category from our annotated dataset.


We evaluate DOQ on several existing datasets and show that it compares favorably with existing methods that require more resources and have less coverage.

we identify and correct problems with some of the existing datasets resulting in new, cleaner, evaluation datasets.


For the first level, we employ an encoder to obtain a sentence representation v x for each input sentence segment, which can be used as high-level features for sentence classification.

the encoder is followed by a fully-connected (FC) layer and a softmax layer to estimate the probability of each level-1 sentence function.


If the predicted label is not the target sentence function, this candidate response's score will be penalized with a weight by the Classification-for-Modeling classifier's output probability scaled by a constant.

assume the IR baseline f (x, R) → {s 1 , s 2 , .


We then run the Classification-for-Modeling classifier to predict the sentence function d r i for each candidate response r i with the probability p r i .

a penalty weight is computed for each candidate as: if the candidate response r i is assigned with the target sentence function, s penalty i is zero.


However, holistic essay scoring technologies are far from adequate for use in classroom settings, where providing students with feedback on how to improve their essays is of utmost importance.

merely returning a low holistic score to an essay provides essentially no feedback to its author on which aspect(s) of the essay contributed to the low score and how it can be improved.


As another example, researchers working on automated essay revision , where the goal is to revise, for instance, a thesis statement or an argument in an essay to make it stronger, would benefit from having the thesis strength scores we annotate.

the first step in deciding how to revise a thesis statement to make it stronger is to understand why it is weak, and the aforementioned attributes that we propose to annotate will provide insights into what makes a thesis statement weak and subsequently how to revise it.


In contrast, while a thesis statement is expected to provide evidences in support of the claim it states, the strength of the thesis statement does not depend on the strength of the support.

while persuasiveness is adversely affected by the presence of weak evidences in the argument, thesis strength is adversely affected by the absence of evidences rather than the presence of weak evidences.


We found that the "mostly yes" and "mostly no" cases are the most difficult for the annotators to agree on.

some annotators translate a "mostly yes" to a '3' while others translate it to a '2', and similarly for a "mostly no".


Next, we conduct an oracle experiment to determine how well these 10 attributes, when used in combination, can explain Thesis Strength.

we train two models on the 793 essays that have a thesis statement to score a thesis statement's strength using the gold attributes as features.


Our next experiment exploits this benefit.

we compute the P C values between our Thesis Strength scores and the scores annotated by Persing and his colleagues (2010;   the essay?


Given the close connection between a major claim and a thesis, we approximate the thesis statement identification task as a major claim identification problem.


Given the difficulty of thesis statement identification, we conduct our thesis strength scoring experiments using gold thesis statements.

(we exclude essays without thesis statements in these experiments.)


(In other words, we exclude essays without thesis statements in these experiments.)

we train a model that takes a gold thesis statement as input and predicts its strength score.


In this system, we train a model to predict thesis strength based on the entire essay.

we use as input all the sentences in the essay.


For each of these systems, we employ (1) a linear SVM regressor and (2) a NN as the underlying model.

to train the first system, the SVM/NN we use is the same as that in the thesis statement identification experiment except that (1) the input is the gold thesis and (2) the output is the thesis strength score.


To our knowledge, corpora with sentence-wise emotion annotations are not available for this language.

there is no German corpus with speakers' descriptions of emotionally intense events similar to the English ISEAR.


Here we present the Meta-Info Guided Aggregation (MIGA) model, a new selfaggregation model whose aggregation process is guided by the meta-info associated with each individual short text.

mIGA aggregates short texts according to two factors: whether those texts have similar content and whether they share similar meta-info.


For each latent cluster m: where Ga(•, •) is the gamma distribution with the shape and rate parameters; The main idea of MIGA is the meta-info guided aggregation, where instead of putting an uninformative prior on ψ d , MIGA constructs an informative document-specific Dirichlet prior with parameter π d computed from the document's labels.

in Step 3a above, λ l,m captures the correlations between label l and cluster m. If document d has label l, i.e., f d,l = 1, λ l,m contributes to π d,m , which is the prior of ψ d,m .


Although encouraging the invertible constraint leads to slightly poorer performance of f −1 de on unsupervised tasks, it generally leads to better sentence representations when the ensemble of the encoder f en and the inverse of the decoder f −1 de is considered.

for unsupervised tasks, the ensemble is an average of the two vector representations produced from the two encoding functions during the testing time, and for supervised tasks, the concatenation of the two representations is regarded as the representation of a given sentence.


k σ k (H) = 1, which we specify further below in detail.


Using X and Y, a linear mapping T can be learned, which "aligns" the X and Y space.

here Ω is a constraint set on T. For example, Mikolov et al.


Since hubness results in some y j 's being retrieved more frequently than others, a natural idea is to force all y j 's being equally preferred to be retrieved.

the preference of y j can be measured by on average, how the j-th target word is likely to be picked as the translation of a source word.


We observe a large variance when either the source or target language is German.

the equal preference assumption is more violated when translating from or to German.


In order to address this issue, we, jointly with the MIL model, learn a classifier which detects potentially problematic candidate lists.

the classifier predicts how likely a given list is noisy (i.e., how much we should trust it).


For instance, the TV episode BILL CLINTON is associated with several types including BASE.TYPE ONTOLOGY.NON AGENT and TV.TV SERIES EPISODE.

in order to produce an entity embedding, each type t is assigned a vector t ∈ R dt .


Presumably the ones less consistent with the predictions of the EL model.

joint training of EL and ND models encourages learning an entitylinking scoring function consistent with a large proportion of the data set but not necessarily with the entire data set.


In the previous work, the distribution of AL problems is constructed via simulations on a related task for which enough labelled data exist.

the labelled data is randomly partitioned into the training, evaluation, and pool of unlabelled (by pretending the labels are unobserved) datasets.


We further investigate the combination of transferring the policy network with transferring the underlying learner.

we first train a classifier on all of the annotated data from the source domain/language; this classifier is then transferred to the target task and further fine-tuned using the collected labelled data.


In bilingual and same annotation scenarios, our dream-based transfer method consistently outperforms other datadriven AL query strategy learning and heuristic methods.

diversity-based query strategy performs badly in almost every case because it ignores the labelling information.


We further evaluate our methods in the sce- nario where the source and target tasks have different characteristic.

we conduct experiment on cross-domain cross-annotation NER.


In this paper, we formulate the learning of OOV embeddings as a few-shot regression problem, and address it by training a representation function to predict the oracle embedding vector (defined as embedding trained with abundant observations) based on limited observations.

we propose a novel hierarchical attention-based architecture to serve as the neural regression function, with which the context information of a word is encoded and aggregated from K observations.


Inspired by this, we propose an attentionbased hierarchical context encoder (HiCE), which can leverage both sentence examples and morphological information.

the proposed model adopts multi-head self-attention to integrate information extracted from multiple contexts, and the morphological information can be easily integrated through a character-level CNN encoder.


Instead of simply fine-tuning Fθ on D N , MAML provides a way of learning to fine-tune.

the model is firstly trained on D T to get a more promising initialization, based on which fine-tuning the model on D N with just a few examples could generalize well.


• Additive: Additive model (Lazaridou et al., 2017) is a purely non-parametric algorithm that averages the word embeddings of the masked supporting contexts S t .

: Also, this approach can be augmented by removing the stop words beforehand.


•à la carte: This algorithm (Khodak et al., 2018) is based on an additive model, followed by a linear transformation A that can be learned through an auxiliary regression task.

: Ae additive w We conduct experiments by using their opensource implementation 5 .


Experimental Results Table 1 lists the performance of HiCE and baselines with different numbers of context sentences.

our method (HiCE+Morph+MAML) 6 achieves the best performance among all the other baseline methods under most settings.


We retrieved available data sources from previous publications (Zhang et al., 2014;He and McAuley, 2016;Huang and Paul, 2018).

we use four different sources in in English-Amazon (music reviews), Yelp (restaurant and hotel reviews), Twitter, and economic newspaper articles ( Figure Eight Inc., 2015)-and one source in Chinese, Dianping (Meituan-Dianping, 2019).


1, we get the final objective function as: where the hyper-parameter λ > 0 is the domain adaptation regularization term that controls the trade-off between the source risk and the domain divergence terms.

it controls how much weight mass is put on generalizable common representation v/s the source specific representation.


On the contrary, the objective used in the paper is based on the min-max optimization criterion that minimizes the domain specific component loss as well as maximizes the shared component loss.

the proposed approach not only models the similarity between domains but also models and mitigates the source domain specific information, thus leading to better cross-domain performance.


The primary objective of the common representation is to make the source and target distributions appear similar.

these representation should be such that it becomes arduous to distinguish between the source and target examples for a model trained on this representation.


The three repetitions are regularized with certain conditions, such as the frequency distribution of the named entity types in a named entity recognition (NER) task, to reduce the unintentional introduced difference in the frequency distributions between the training and validation sets due to the random partitioning of a corpus and to make the comparison more reliable.

the m × 2 BCV estimator of certain evaluation metrics pos-sesses a minimum variance, which ensures that the tests on the 3 × 2 BCV have higher powers and replicabilities (Wang et al., 2014(Wang et al., , 2017b.


The confusion matrix on T is M = (TP, FP, FN, TN), where TP, FP, FN, and TN stand for true positive, false positive, false negative, and true negative, respectively.

from these counts, one can compute the P, R and f 1 : Goutte and Gaussier (2005) provided the natural probabilistic interpretations of P and R. M follows a multinomial distribution with parameters π Then, P and R estimate the following probabilities: p = P (l = +|z = +), r = P (z = +|l = +), (2) where l and z represent the true and predicted labels and + indicates a positive label.


Thus, the decisions of the Bayes test are remarkably different.

the posterior distributions of P, R, and F 1 are given in Figure 3, which indicate that the improvement in B is not evident.


We present here a general-purpose addition to the standard seq2seq framework that aims to simultaneously tackle all of the above issues.

we propose Scratchpad, a novel write mechanism that allows the decoder to keep notes on its past actions (i.e., generation, attention, copying) by directly modifying encoder states.


Previous work based on coverage based approaches (Tu et al., 2016;See et al., 2017) either imposed an extra term to the loss function or used an extra vector to keep track of which parts of the input sequences had been attended to, thereby focusing the attention weights in subsequent steps on tokens that received little attention before.

focusing the attention on the relevant parts of the input.


Pointer Networks (Vinyals et al., 2015) address this limitation by using attention (Bahdanau et al., 2015) as a pointing mechanism.

an encoder network first converts the input sequence X = (x 1 , .


Like LSTM (Hochreiter and Schmidhuber, 1997), GRU cells are also designed to capture long range dependencies, but have fewer parameters than LSTM cells.

our encoder uses six (6) recurrent layers of BiGRU cells, and generates hidden states H = (h 1 , .


Upon investigation, we found that their experimental setting does not match with the standard one.

when extracting the sentences from the RST-DT dataset, instead of using gold tokenization, they use an automatic tokenizer, which gives fewer sentences -865 test sentences instead of 991 and 6132 training sentences instead of 7673.


Unfortunately, such multitask learning models for IDRR share the limitation of failing to exploit the mapping between the implicit connectives and the discourse relations.

each implicit connective in the PDTB dataset can be naturally mapped into the corresponding discourse relations based on their semantics that can be further employed to transfer the knowledge from the connectives to the relations.


The multi-task learning model for IDRR aims to predict the discourse relations and the implicit connectives simultaneously in a single training process so the knowledge from the connective prediction can be transferred to the relation prediction to improve the performance.

the arguments A 1 and A 2 are first consumed by a neural network model M (called the encoder model) to generate a representation vector V = M (A 1 , A 2 ).


This section compares our proposed model with the current state-of-the-art models for IDRR.

table 1 reports the performance of the models for the one-versus-other binary classification settings while table 2 shows the performance of the models for the multi-class classification settings (i.e., 4-way and 11-way with PDtB-Lin and PDtB-Ji) on the corresponding test sets.


Our main observation in this paper is that while models for learning script knowledge improved significantly over the last decade, these models can essentially represent only a single event relationship, co-occurrence.

events appearing in similar contexts tend to have similar representations.


", called Step 1, any of the following events in Step 2 would be highly related, and thus similar, to the input event.

"It was raining outside" and "She was very hungry" are both possible NEXT events.